Cameras are commonly used to capture an image of a scene that includes one or more objects. Unfortunately, some of the images can be blurred. For example, movement of the camera and/or movement of the objects in the scene during the exposure time of the camera can cause the image to be blurred. Further, if the camera is not properly focused when the image is captured, the image can be blurred. Additionally, blur can be caused by lens aberrations in the image apparatus.
When blur is sufficiently spatially uniform, a blurred captured image can be modeled as the convolution of a latent sharp image with some point spread function (“PSF”) plus noise,B=K*L+No.  Equation (1)In Equation 1 and elsewhere in this document, (i) “B” represents a blurry image, (ii) “L” represents a latent sharp image, (iii) “K” represents a PSF kernel, and (iv) “No” represents noise (including quantization errors, compression artifacts, etc.).
A non-blind deconvolution problem seeks to recover the latent sharp image L when the PSF kernel K is known or already estimated. The non-blind deconvolution problem is a very difficult to accurately solve because it is sensitive to any departures from the underlying mathematical model, estimation errors, and noise in the data. The inverse problem of recovering both the latent sharp image L and the PSF kernel K when only the blurry image B is known, is called a blind deconvolution problem.
Moreover, many blurry images include areas that further complicate the problem of recovering a latent sharp image L and/or the point spread function. For example, extremely bright areas where the sensor pixels reach their saturation point in the blurry image B can adversely influence the resulting latent sharp image L. Additionally, salt and pepper noise due to defective pixels in the sensor, and other outliers violating the convolution blurring model tend to reduce the accuracy of the latent sharp image.
The pixels containing extremely bright areas, noise, and other outliers can be clipped during the deconvolution process to reduce the influence on the accuracy of the latent sharp image. Unfortunately, the clipping of these pixels tend to produce severe ringing artifacts in the resulting latent sharp image, which are very hard to avoid. Some special techniques for suppressing these artifacts have been developed, but they tend to be complicated and computationally expensive.
Additionally, many images have strong edge bias, noise, or lack sufficient reliable edge/texture information. As a result thereof, the estimated PSF kernel in many instances is not very accurate.
Further, sophisticated deconvolution methods used for high quality deblurring tend to be very complex and slow. This makes deblurring of high resolution images produced by modern digital cameras too slow for the use in a commercial product used by the general public.
Additionally, certain areas of a blurry image B will have a different blur PSF kernel. Thus, it is often very difficult to accurately determine the PSF kernel K and the latent sharp image L of a blurry image.